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This Special Issue of Remote Sensing focuses on the latest advances in mapping LULC. This Issue intends to provide an overview of the progress made in the methodologies of mapping LULC applications, approaches, and methods. Due to climate variability, territorial dynamics, and societal changes, efficient planning strategies cannot be developed without considering processes related to land cover and land use changes. This requires solutions that support the observation, mapping, monitoring, analysis, and modelling of land-related activities. Remote sensing applications for exploring land covers and land uses have experienced notable progress with the development of current approaches, especially advances in artificial intelligence, the proliferation of remote sensing sensors, and the availability of space and airborne data with ancillary databases and massive geodata processing. The aim of this Special Issue related to the mapping of land covers and land uses by remote sensing is to review the latest methods and to increase the methodological soundness: that is, the consistency, comparability, accuracy, and transparency of assessing, monitoring, and predicting land uses, land covers and their changes using spatial analysis, artificial intelligence, and remote sensing techniques. New advances in the spatial modelling of territorial patterns and their dynamics and evolutions by remote sensing introduce new conceptual questions related to the actual environmental and territorial processes: the mapping of multi-scalar dynamics and different land use and land cover temporalities and changes.

The Garhwal Himalaya has experienced extensive deforestation and forest fragmentation, but data and documentation detailing this transformation of the Himalaya are limited. The aim of this study is to analyse the observed changes in land cover and forest fragmentation that occurred between 1976 and 2014 in the Garhwal Himalayan region in India. Three images from Landsat 2 Multispectral Scanner System (MSS), Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) were used to extract the land cover maps. A cross-tabulation detection method in the geographic information system (GIS) module was used to detect land cover changes during the 1st period (1976–1998) and 2nd period (1998–2014). The landscape fragmentation tool LFT v2.0 was used to construct a forest fragmentation map and analyse the forest fragmentation pattern and change during the 1st period (1976–1998) and 2nd period (1998–2014). The overall annual rate of change in the forest cover was observed to be 0.22% and 0.27% in the 1st period (1976–1998) and 2nd period (1998–2014), respectively. The forest fragmentation analysis shows that a large core forest has decreased throughout the study period. The total area of forest patches also increased from 1976 to 2014, which are completely degraded forests. The results indicate that anthropogenic activities are the main causes of the loss of forest cover and forest fragmentation, but that natural factors also contributed. An increase in the area of scrub and barren land also contributed to the accumulation of wasteland or non-forest land in this region. Determining the trend and the rate of land cover conversion is necessary for development planners to establish a rational land use policy.

Land use and land cover change is an important driving force for changes in ecosystem services. We defined several important human-induced land cover change processes such as Ecological Restoration Project, Cropland Expansion, Land Degradation, and Urbanization by the land use/land cover transition matrix method. We studied human-induced land cover changes in the Yellow River Basin from 1980 to 2015 and evaluated its impact on ecosystem service values by the benefit transfer method and elasticity coefficient. The results show that the cumulative area of human-induced land cover change reaches 65.71 million ha from 1980 to 2015, which is close to the total area of the Yellow River Basin. Before 2000, Ecological Restoration Project was the most important human-induced land cover change process. However, due to the large amount of cropland expansion and land degradation, the area of natural vegetation was reduced and the ecosystem value declined. Since 2000, due to the implementation of the “Grain for Green” program, the natural vegetation of upstream area and midstream area of Yellow River Basin has been significantly improved. This implies that under an appropriate policy framework, a small amount of human-induced land cover change can also improve ecosystem services significantly.

The impacts of vegetation cover changes (VCCs) and land use land cover changes (LULCCs) on climate variabilities need to be addressed while maintaining healthy urban development. This study aimed to evaluate LULCCs and VCCs and their impacts on land surface temperature (LST) and rainfall in the Darbhanga district of Bihar, India. Landsat data and climate hazard group infrared precipitation with stations (CHIRPS) were used to assess LST or LULCCs and rainfall, respectively, over the study area. Results showed a decline in vegetation cover to 11.73% (26,857.43 ha) in the year 2019 from 19.12% (43,733.61 ha) in 1999. Also an increase in built-up and cropland by 4.9% (11,224.6 ha) and 4.38% (10,043.2 ha), respectively, was observed in the last 20 years. With decreasing vegetation cover in the study area, the mean LST increased while the mean annual rainfall decreased for the study period. The Mann–Kendall trend (MKT) test exhibited no significant trends for LST and rainfall with p values of 0.43 and 0.69, respectively, although Sen’s slope indicated variability in LST and rainfall even though it was insignificant over the study area. The study would identify areas requiring immediate action for saving its resources and providing insight into upholding its competence. The findings would also lead to proper decision making for the concerned stakeholders, assuring sustainability in appropriate planning of land use and utilisation of resources and maintain the agroclimatic condition.

Habitat loss is a key driver of biodiversity loss. However, hardly any long-term time series analyses of habitat loss are available above the local scale for finer-level habitat categories. We analysed, from a long-term perspective, the habitat specificity of habitat-area loss, the change in trends in habitat loss since 1989 (dissolution of the communist state), and the impact of protected areas on habitat loss in Hungary. We studied 20 seminatural habitat types in 5000 randomly selected localities over 7 periods from 1783 to 2013 based on historical maps, archival and recent aerial photos and satellite imagery, botanical descriptions, and field data. We developed a method for estimating habitat types based on information transfer between historical sources (i.e., information from a source was used to interpret or enrich information from another source). Trends in habitat loss over time were habitat specific. We identified 7 types of habitat loss over time regarding functional form: linear, exponential, linear and exponential, delayed, minimum, maximum, and disappearance. Most habitats had continuous loss from period to period. After 1986 the average annual rates of habitat loss increased, but the trend reversed after 2002. Nature conservation measures significantly affected habitat loss; net loss was halted, albeit only inside protected areas. When calculating the degree of endangerment based on short-term data (52 years), we classified only 1 habitat as critically endangered, but based on long-term data (230 years), this increased to 7 (including habitat that no longer existed). Hungary will probably reach the global Convention on Biological Diversity Target 5 but will probably not achieve the EU Biodiversity Strategy target of halting habitat loss by 2020. Long-term trend data were highly useful when we examined recent habitat-loss data in a wider context. Our method could be applied effectively in other countries to augment shorter-term data sets on trends in habitat area. La pérdida de hábitats es un conductor importante de la pérdida de la biodiversidad. Sin embargo, difícilmente está disponible una serie de análisis a largo plazo por encima de la escala local para categorías de hábitat de un nivel más fino. Analizamos, desde una perspectiva de largo plazo, la especificidad del hábitat en la pérdida del área de hábitats, el cambio en las tendencias de pérdida de hábitats desde 1989 (disolución del estado comunista), y el impacto de las áreas protegidas sobre la pérdida de hábitat en Hungría. Estudiamos 20 tipos de hábitats seminaturales en 5000 localidades seleccionadas al azar a lo largo de siete periodos desde 1783 hasta 2013 con base en mapas históricos, fotografías aéreas recientes y de archivos e imágenes de satélites, descripciones botánicas, y datos de campo. Desarrollamos un método para estimar los tipos de hábitats basado en la transferencia de información entre las fuentes históricas (es decir, se usó información a partir de una fuente para interpretar o enriquecer la información proveniente de otra fuente). Las tendencias en la pérdida de hábitats fueron específicas por hábitat. Identificamos siete tipos de pérdida de hábitats a través del tiempo con respecto a la forma funcional: lineal, exponencial, lineal y exponencial, retrasada, mínima, máxima, y desaparición. La mayoría de los hábitats tuvieron una pérdida continua de un periodo o a otro. Después de 1986, las tasas anuales promedio de la pérdida de hábitats incrementaron, pero la tendencia se revirtió después del 2002. Las medidas de conservación de la naturaleza afectaron considerablemente a la pérdida de hábitats; se detuvo la pérdida neta, no obstante sólo fue dentro de las áreas protegidas. Cuando calculamos el grado de peligro basado en información de corto plazo (52 años), solamente clasificamos a un hábitat como en peligro crítico, pero con base en la información de largo plazo (230 años), esta clasificación incrementó a siete hábitats (incluyendo a un hábitat que ya no existía). Hungría probablemente alcanzará el Objetivo 5 global de la Convención sobre la Diversidad Biológica pero probablemente no sea el caso para el objetivo de detener la pérdida de hábitats para el 2020 impuesto por la Estrategia de Biodiversidad de la UE. La información de largo plazo sobre las tendencias fue muy útil cuando se examinaron datos recientes de pérdida de hábitats en un contexto más amplio. Nuestro método podría aplicarse efectivamente en otros países para aumentar los conjuntos de datos de corto plazo sobre las tendencias en áreas de hábitat. 生境丧失是生物多样性丧失的ー个关键驱动力。然而，目前几乎没有对局部尺度、高精度生境分类下的 生境丧失的长期时序分析。我们从长期的视角分析了匈牙利生境面积减少的生境特异性、自 1989年以来生境 丧失的变化趋势以及保护地对生境丧失的作用。我们利用历史地图、档案ヽ近期的航片及卫星影像、植物记录 和野外数据，在 5000 个随机选择的地区,研究了 20 个半自然生境类型从 1783 年到 2013 年的 7 个时间段内 的变化。我们建立了一个基于历史来源之间信息传递(即ー个来源的信息用于解释或完善另ー个来源的信息) 的方法来估计生境类型。生境丧失随时间发展的变化趋势有生境特异性。我们根据函数类型分出7 类随时间 发生的生境丧失: 线性、指数型、线性及指数型、延迟型、最小型、最大型以及生境消失。大部分生境都随着 时间流转持续地丧失。在 1986 年之后, 生境丧失的平均年变化率増加, 而这个趋势在 2002 年后有所逆转。自 然保护措施有效遏制了生境丧失，使净减少停止’ 尽管这一影响仅在保护地之内。当根据短期数据(52 年)计算 瀕危程度时, 只有一个生境被评为极度瀕危, 但根据长期数据(230 年) 评估’极度瀕危的生境増加到了 7 个（包 括已经不存在的生境) 。匈牙利很可能完成全球《生物多祥性公约》的第五个目标, 但可能难以达到〈〈欧盟生物 多样性战略》在 2020 年前遏止生境丧失的目标。长期变化趋势可用于在更大的背景下检验近期生境丧失的数 据。我们的方法可以有效应用于其它国家, 来补充生境面积变化的短期数据。

Riverine dissolved inorganic carbon (DIC), essential for understanding terrestrial carbon cycling, is undergoing dramatic changes due to climate change and human disturbances. Quantifying how these changes impact DIC fluxes from land to rivers has remained challenging due to limited long‐term data and complex, interacting drivers. Here we ask the question: How and to what extent do climate and land‐cover changes distinctively influence long‐term seasonal and annual trends of DIC production and export? We developed a reactive transport model, constrained by three decades of streamflow, DIC, and carbon isotope data, for a karst catchment in southwest China simultaneously experiencing a warmer, drier climate (increasing aridity) and forest recovery. Results show that from 1980 to 2010s, precipitation has declined from 1,261 to 1,005 mm/yr, and discharge from 700 to 552 mm/yr, with no significant change in evapotranspiration. DIC production and export have declined at the rates of 2.3 × 105 and 5.4 × 105 mol C/yr/yr, respectively. Drier climate and reduced discharge diminish carbonate weathering but also store more produced DIC, resulting in higher DIC concentrations over time but a twofold decline in DIC export compared to its production. Interestingly, although forest recovery elevates organic carbon content, cooling soils and lower soil moisture reduce rates of soil respiration. Scenario analysis shows that forest recovery accounts for 91% of the production decline, while increasing climate aridity explains 78% of the export reduction. Seasonal analysis further reveals that soil respiration declines most during hot‐wet seasons but calcite weathering drops more in cold‐dry seasons. These findings underscore the differential impacts of climate and land‐cover changes on carbon transport and transformation processes, which are crucial for understanding carbon cycling and budgets under evolving environmental conditions.

Indian Himalayan basins are earmarked for widespread dam building, but aggregate effects of these dams on terrestrial ecosystems are unknown. We mapped distribution of 292 dams (under construction and proposed) and projected effects of these dams on terrestrial ecosystems under different scenarios of land-cover loss. We analyzed land-cover data of the Himalayan valleys, where dams are located. We estimated dam density on fifth- through seventh-order rivers and compared these estimates with current global figures. We used a species-area relation model (SAR) to predict short- and long-term species extinctions driven by deforestation. We used scatter plots and correlation studies to analyze distribution patterns of species and dams and to reveal potential overlap between species-rich areas and dam sites. We investigated effects of disturbance on community structure of undisturbed forests. Nearly 90% of Indian Himalayan valleys would be affected by dam building and 27% of these dams would affect dense forests. Our model projected that 54,117 ha of forests would be submerged and 114,361 ha would be damaged by dam-related activities. A dam density of 0.3247/1000 km 2 would be nearly 62 times greater than current average global figures; the average of 1 dam for every 32 km of river channel would be 1.5 times higher than figures reported for U.S. rivers. Our results show that most dams would be located in species-rich areas of the Himalaya. The SAR model projected that by 2025, deforestation due to dam building would likely result in extinction of 22 angiosperm and 7 vertebrate taxa. Disturbance due to dam building would likely reduce tree species richness by 35% tree density by 42%, and tree basal cover by 30% in dense forests. These results, combined with relatively weak national environmental impact assessment and implementation, point toward significant loss of species if all proposed dams in the Indian Himalaya are constructed. Las cuencas del Himalaya Hindú están destinadas para la construcción extensiva de presas, pero se desconocen los efectos agregados de estas presas sobre los ecosistemas terrestres. Mapeamos la distribución de 292 presas (en construcción y propuestas) y los efectos proyectados de estas presas sobre los ecosistemas terrestres bajo diferentes escenarios de pérdida de cobertura de suelo. Analizamos datos de cobertura de suelo de los valles del Himalaya, donde se localizan las presas. Estimamos la densidad de presas en ríos de quinto a séptimo orden y comparamos estas estimaciones con cifras globales actuales. Utilizamos un modelos de relación especies-área (REA) para predecir extinciones de especies a corto y largo plazo provocadas por la deforestación. Usamos gráficas de dispersión y estudios de correlación para analizar los patrones de distribución de especies y presas y para revelar el traslape potencial entre áreas ricas en especies y los sitios de las presas. Investigamos los efectos de la perturbación sobre la estructura de la comunidad de bosques no perturbados. Casi 90% de los valles del Himalaya Hindú pudiera ser afectado por la construcción de presas y 27% de estas presas podría afectar bosques densos. Nuestro modelo proyectó que 54,117 ha de bosques quedarían sumergidas y 114,361 ha serían dañadas por actividades relacionadas con las presas. Una densidad de presas de 0.3247/1000 km 2 sería casi 62 veces mayor que las cifras globales promedio actuales; el promedio de 1 presa por cada 32 km de río sería 1.5 veces mayor que la cifra registrada en ríos de E.U.A. Nuestros resultados muestran que la mayoría de las presas estaría localizada en áreas ricas en especies del Himalaya. El modelo REA proyectó que, en 2025, la deforestación debido a la construcción de presas probablemente resultaría en la extinción de 22 taxa de angiospermas y 7 de vertebrados. La perturbación debido a la construcción de presas probablemente reduciría la riqueza de especies de árboles en 35%, la densidad de árboles en 42% y la cobertura basal de árboles en 30% en los bosques densos. Estos resultados, combinados con una evaluación e implementación de impacto ambiental relativamente débil, apuntan hacia una pérdida significativa de especies si son construidas todas las presas propuestas en el Himalaya Hindú.

Climate change and land‐use change are having substantial impacts on biodiversity world‐wide, but few studies have considered the impact of these factors together. If the combined effects of climate and land‐use change are greater than the effects of each threat individually, current conservation management strategies may be inefficient and/or ineffective. This is particularly important with respect to freshwater ecosystems because freshwater biodiversity has declined faster than either terrestrial or marine biodiversity over the last three decades. This is the first study to model the independent and combined effects of climate change and land‐use change on freshwater macroinvertebrates and fish. Using a case study in south‐east Queensland, Australia, we built a Bayesian belief network populated with a combination of field data, simulations, existing models and expert judgment. Different land‐use and climate scenarios were used to make predictions on how the richness of freshwater macroinvertebrates and fish is likely to respond in future. We discovered little change in richness averaged across the region, but identified important impacts and effects at finer scales. High nutrients and high runoff as a result of urbanization combined with high nutrients and high water temperature as a result of climate change and were the leading drivers of potential declines in macroinvertebrates and fish at fine scales. Synthesis and applications. This is the first study to separate out the constituent drivers of impacts on biodiversity that result from climate change and land‐use change. Mitigation requires management actions that reduce in‐stream nutrients, slows terrestrial runoff and provides shade, to improve the resilience of biodiversity in streams. Encouragingly, the restoration of riparian habitats is identified as an important buffering tool that can mitigate the negative effects of climate change and land‐use change.

In order to assess the effects of climate change and land use change on Rawal Dam, a major supply of water for Rawalpindi and Islamabad, this study uses hydrological modeling at the watershed scale. The HEC-HMS model was used to simulate the hydrological response in the Rawal Dam catchment to historical precipitation. The calibrated model was then used to determine how changes in land use and climate had an impact on reservoir inflows. The model divided the Rawal Dam watershed into six sub-basins, each with unique features, and covered the entire reservoir’s catchment area using data from three climatic stations (Murree, Islamabad Zero Point and Rawal Dam). For the time spans of 2003–2005 and 2006–2007, the model was calibrated and verified, respectively. An excellent fit between the observed and predicted flows was provided by the model. The GCM (MPI-ESM1-2-HR) produced estimates of temperature and precipitation under two Shared Socioeconomic Pathways (SSP2 and SSP5) after statistical downscaling with the CMhyd model. To evaluate potential effects of climate change and land use change on Rawal Dam, these projections, along with future circumstances for land use and land cover, were fed to the calibrated model. The analysis was carried out on a seasonal basis over the baseline period (1990–2015) and over future time horizon (2016–2100), which covers the present century. The findings point to a rise in precipitation for both SSPs, which is anticipated to result in an increase in inflows throughout the year. SSP2 projected a 15% increase in precipitation across the Rawal Dam catchment region until the end of the twenty-first century, while SSP5 forecasted a 17% increase. It was determined that higher flows are to be anticipated in the future. The calibrated model can also be utilized successfully for future hydrological impact assessments on the reservoir, it was discovered.

Aim To study global patterns and temporal changes in the seasonal dynamics (quantity and seasonal distribution) of terrestrial gross carbon uptake in response to global environmental change. Location Global. Time period 2000–2016. Major taxa studied Terrestrial ecosystems. Methods Following a phenology‐based definition of photosynthetic seasonality, we decompose gross primary production (GPP) into three periods, green‐up, maturity and senescence, and derive their corresponding GPP (GPPgp, GPPmp and GPPsp, respectively) from a newly developed time series of satellite‐based global GPP to study spatio‐temporal dynamics of seasonal GPP. Results We find that the global fraction of GPPsp (19.8%) is larger than GPPgp (14.3%), indicating a globally asymmetric seasonal distribution of gross carbon uptake by terrestrial ecosystems. Globally, GPPmp plays a dominant role in shaping spatial patterns and increasing/decreasing trends in GPP, while GPPgp/GPPsp contributes to increasing GPP at the regional scale. Higher fractions of GPPgp/GPPmp (lower of GPPsp), as well as the co‐occurrence of increasing GPP and non‐tree vegetation cover in major croplands, are likely to be caused by agricultural intensification. Global changes in GPPgp and GPPsp are closely related to changes in their seasonal distributions (R = .86/.8, respectively), whereas this relationship is weaker for GPPmp (R = .53). Finally, high correlations are observed between changes in GPPgp and GPPsp and changes in their durations (R = .78/.78, respectively), while GPPmp shows a relatively lower correlation with its duration (R = .67). Main conclusions The asymmetric spatio‐temporal patterns in the seasonal dynamics of global terrestrial gross carbon uptake found here have been substantially reshaped by anthropogenic land‐use/cover changes and changes in photosynthetic phenology. Compared to calendar‐based meteorological seasons more suitable for temperate/subpolar ecosystems, our phenology‐based approach is expected to provide an alternative starting point for a better understanding of global spatio‐temporal changes in the seasonal dynamics of terrestrial ecosystem processes and functioning under accelerating global change.